Abstract

Background: There has been rapid growth in ever and current electronic cigarette (e-cigarettes) use over the past 4 years. Use is highest among adolescents and current cigarette smokers. This trend follows a rise in the number of overweight and obese adolescents and teens. This study examined the relationship between BMI and the use of both electronic and conventional cigarette in high school aged youth. Methods: Data from the Youth Risk Behavior Surveillance System (YRBSS)-a nationally representative survey of 9th through 12th grade students conducted every two years-was used for analysis. Two different statistical models-a multinomial logit (mlogit) and quantile regression (QR) of BMI levels-tested the relationship between BMI and electronic/conventional cigarette use among adolescents. Results: BMI was positively related to age, race, and ethnicity. Those who use either conventional or electronic cigarettes had higher BMI than those using neither product. Magnitude vary by demographic groups, particularly among women. While solitary product use was associated with elevated weight, simultaneous use of both products was associated with lower BMI values. Overweight and obese adolescents were more frequent tobacco users than other weight classes. Associations were impervious to model specification. Conclusion: Findings showed that use of either conventional and electronic cigarettes was associated with higher BMI. However, these findings suggest the need for additional research into the health impacts of substance use by adolescents. Research into the physical, emotional and psychological health impacts of electronic tobacco needs to be fully explored as it continues to grow in popularity among adolescents.

Keywords

BMI, Adolescence, E-Cigarette, Vaping, Smoking.

Research Implications

Using a large, nationally representative database of adolescents,
this study provides examines the relationship between BMI and
various forms of cigarette use. Results show that regular use of
conventional cigarettes is associated with higher BMI increases
adolescent weight impacts vary of conventional cigarette,
electronic cigarette and dual product use by youth age 12 to 18.
Results show that, while electronic cigarettes result in a lower
BMI compared to non-users. The growing popularity of vaping
products signals the need for additional examination of the
health effects of these devices.

Introduction

Use of electronic cigarettes (e-cigarettes) among young adults
has grown substantially in recent years [1,2]. E-cigarette use is
highest among middle and high school students [3]. Although
research has documented a relationship between conventional
cigarette smoking and adolescent weight outcomes [4-8] few
comprehensive, nationally representative studies have evaluated the correlation between adolescent weight (body mass index)
and electronic tobacco use.

As electronic tobacco use becomes wide spread among youth
[9-11], it is important to gain a better understanding of the
relationship between these products and BMI. The current
study examines weight status as a correlate of electronic and
conventional cigarette. E-cigarettes could serve as a gateway to
other forms of tobacco use. Among those who try e-cigarettes,
25% had taken at least one puff on a cigarette within the next
year, compared to only 10% of those who never tried vaping
[12,13].

Many are under the impression that because e-cigarettes do not
contain tobacco, they pose little health risk, but the consequences
of vaping and dual use have yet to be fully explored [14]. A
handful of studies have linked e-cigarette usage to bleeding
mouths and throats, gum disease, cancer, delayed wound
healing, coughing and bronchitis [15].

A third of e-cigarette users are nonsmokers, suggesting that
e-cigarettes contribute to primary nicotine addiction and renormalization of tobacco [1]. Recent evidence also shows
elevated levels of dual use, with over 80% of current high school
e-cigarette users concurrently smoking conventional cigarettes
[10,16].

While e-cigarettes were originally used by those hoping to
quit smoking, motivations appear to be evolving [17,18]. In
2012, 85% of e-cigarette users reported using e-cigarettes as
a cessation aid [19-21]. By 2015, less than 30% cited quitting
smoking regular cigarettes as a reason for vaping. Most reported
using e-cigarettes because of expeditious consumption, ease of
concealment, accessibility, healthier than tobacco cigarettes,
and aesthetically pleasing [22].

However, studies are not consistent in the assertion the
e-cigarettes serve as a gateway to other forms of tobacco. Low
prices, ease of access, targeted advertising and popularity could
contribute to initiation and use [23]. The cigarette industry has
a significant online presence and promotes electronic cigarettes
as a safer alternative to conventional cigarettes [23]. Therefore,
the willingness to try e-cigarettes may vary by the degree to
which individuals are influenced by marketing. E-cigarettes
may appeal to adolescents with novelty-seeking characteristics
[24]. While older smokers may use e-cigarettes to quit smoking,
intention to quit does not play a crucial role in the e-cigarette
use among the young [7]. Conventional and e-cigarettes could
appeal to young people in the process of forming a smoker
identity [24]. Also, peer cigarette smoking has a significant
association with e-cigarette use in adolescent nonsmokers, and
this association was greater on never than former smokers [14].
This analysis, however, does not attempt to determine causality
of motivation for use.

Studies examining the relationship between BMI and
conventional cigarette use have found a positive impact on
adolescent BMI [25-27]. Many cite high BMI is a risk factor
for smoking initiation as it is often used to curb appetite. Few
studies have linked BMI and e-cigarettes. One adult study found
that those who vape for weight loss/control, or to prevent post
smoking-cessation weight gain were more successful [28].
One regional study linked vaping to higher weight and showed
robust correlations [29].

This analysis uses data from the Youth Risk Behavior
Surveillance System (YRBSS)-a nationally representative
survey that monitors health risk behaviors among high school
students in 9th through 12th grade-to examine the association
between these behaviors and adolescent BMI. Multinomial
logistic regressions and quantile regression estimate this
relationship. The paper proceeds with a description of the data
and methodology. Section III presents results. Finally, Section
IV briefly summarizes the primary findings and topics for
additional research.

Materials and Methods

The YRBSS was established by the CDC to monitor the
prevalence of health‐related behaviors in U.S. youth. It contains
information on risky behavior established during childhood and
early adolescence including sexual behavior, alcohol and drug
use, physical activity, tobacco use, behaviors that contribute to
unintentional injuries, violence and unhealthy dietary behavior.
The YRBSS also monitors the prevalence of obesity, asthma and other priority health-related behaviors. It is conducted every
other year beginning in 1991 with 2015 being the most recent
available.

Subjects included boys and girls ages 12 to 18 years enrolled
in 9th through 12th at a public and private school. Average
respondent age is 15 to 16 (Table 1). Racial groups included
whites, blacks and Hispanics. Other ethnic groups (American
Indians and Pacific Islanders) were not separately classified.
The sample is roughly 20% black and 27% Hispanic with equal
percentages of males and females.

Covariate Descriptive Statistics

Variables

Mean

Std Dev

Min

Max

Males

Age

5.222

1.235

1

7

BMI percentile

64.258

28.83

0

99.96

BMI

23.832

4.966

13

55

BMI category

0.967

0.948

0

3

Black

0.213

0.41

0

1

Hispanic

0.272

0.445

0

1

Uses E-Cigarettes

0.28

0.449

0

1

Daily smoker

0.074

0.262

0

1

Female

Age

5.112

1.229

1

7

BMI percentile

61.218

27.691

0

99.8

BMI

23.1

4.819

13

55

BMI category

0.881

0.842

0

3

Black

0.232

0.422

0

1

Hispanic

0.275

0.447

0

1

Uses E-Cigarettes

0.23

0.421

0

1

Daily smoker

0.054

0.226

0

1

Table 1a: Covariate descriptive statistics.

YRBSS: Categorical Covariate Frequencies

Male

Female

Age

N

Percent

Age

N

Percent

Age

12

150

0.16

12

109

0.12

13

99

0.11

13

104

0.11

14

7841

8.54

14

9647

10.25

15

20005

21.78

15

21719

23.09

16

23314

25.39

16

24219

25.74

17

23889

26.01

17

24298

25.83

18

16532

18

18

13985

14.86

BMI

Underweight

32781

35.7

Underweight

33424

35.53

Normal Weight

39442

42.95

Normal Weight

44586

47.39

Overweight

9501

10.35

Overweight

9871

10.49

Obese

10106

11.01

Obese

6200

6.59

Currently used electronic vapor products

No

5340

71.97

No

5767

76.95

Yes

2080

28.03

Yes

1727

23.05

Currently smoked cigarettes daily

No

80487

92.61

No

85757

94.62

Yes

6427

7.39

Yes

4880

5.38

Table 1b: YRBSS: Categorical covariate frequencies.

Weight Status

BMI

Underweight

Below 18.5

Normal or Healthy weight

Between 18.5 and 24.9

Overweight

Between 25.0 and 29.9

Obese

30 and above

Table 1c: BMI-for-age percentile.

BMI ranges from 13 to 55 for males and females with an
average of 23. Based on CDC recommendations, respondents
are placed into weight categories—underweight, normal
weight, overweight and obese-by their BMI ranges listed below.
Roughly 35% of males and females are underweight-less than
18.5 BMI-and 40% normal weight-between 18.5 and 24.9 BMI.
The remaining 25% are either overweight or obese with slightly
more obese males than females. Means are listed in Table 1. The
lower panel shows the age and BMI disaggregation.

The BMI-for-age percentiles are also provided in Table 1,
percentile rank weight compared to that of others of the same
age and sex. While adolescent BMI can be interpreted as both
a raw value and a percentile, given that males and females are
analyzed separately within the same age range, the form of use
did not significantly change results. Therefore, BMI is utilized
for ease of interpretation.

YRBSS respondents are between ages 12 and 18. These,
however, are recorded using values of one through seven. A
mean age of five is roughly 16 years of age. The survey captures
electronic and conventional cigarette use. Two primary elements
from the YRBSS are used. First, youth are considered current
conventional smokers if they report having smoked at least one
cigarette in the last 30 days. Roughly 7.5% and 5.5% of males
and females respectively are conventional cigarette smokers.
Second, youth are considered current e-cigarette users if they
have used electronic vapor products on one of the last 30 days-
28% and 23% of males and females.

In these data electronic cigarettes refers to vapor products, such
as blu, NJOY, Vuse, MarkTen, Logic, Vapin Plus, eGo, and Halo.
Electronic vapor products include e-cigarettes, e-cigars, e-pipes,
vape pipes, vaping pens, e-hookahs, and hookah pens. Vaping is
also used interchangeably in this work. Conventional cigarettes
are considered tobacco-containing commercial cigarettes.
Conventional and e-cigarette use is captured with binary
dummy variables. All analyses are run separately for males
and females. YRBSS oversamples racial and ethnic groups to
enable small sample subsets, therefore estimates are weighted to
mimic national demographic profiles. YRBSS sample weights
are used as recommended in the data documentation. Statistical
work uses SAS software. Due to time and cohort heterogeneity,
survey stratum is used as the analysis class.

Most respondents are non-smokers with only five percent of
overweight and obese respondents report smoking conventional
cigarettes, but 30% use electronic cigarettes. Most e-cigarette
smokers have BMI within the under or normal weight range
suggesting an apparent association between lower BMI and use.
However, means cannot properly address the behavioral impact on BMI therefore, two forms of regression are used to assess the
BMI relationship. First, multinomial logistic regression models
test the probability of having a high BMI category given cigarette
or e-cigarette use. Second, quantile regression describes the
relationship between BMI and smoking at different points in
the conditional BMI. The relationship between cigarette use and
BMI could vary along the BMI distribution and QR considers the
impact of smoking on weight along the entire BMI distribution,
not just the conditional mean.

This study faces several limitations. First, all estimates were
based on self-reported data, which might be affected by reporting
bias. Given that estimates resemble others’ findings for the youth
population, findings do hold some legitimacy [30]. Secondly,
the YRBSS contains relatively few demographic control
covariates. It would have been desirable to include income,
region of residence, urban/rural classification, household size
and other factors. In additionally, there is no information on the smoking behavior of friends, family members or peer of the
respondents. This can be significant when considering cigarette
use as social influence is a very strong driver of the initiation
and habituation of behavior among adolescents [31]. Finally,
data did not allow assessment of willingness to quit tobacco
smoking or motivation.

Result

All regressions use the SAS software package. Table 2 includes
results for the multinomial logit. A multinomial logit models
the probability of an individual with the given characteristic
assuming a higher BMI category. For example, a positive
coefficient value for smoking indicates that respondents who
smoke have a higher BMI category than those who do not smoke.
Age is significantly associated with higher BMI categorization
denoting higher BMI and higher ages. Electronic cigarette use
is significantly related to higher BMI among both males and
females, consistent with other findings. Conventional cigarette use is related to substantially higher weight for females, but
not males. The interaction-representing individuals who report
both simultaneously vaping and smoking regularly-is related
negatively associated with BMI. Dual product use could be used
to curb eating or in place of other types of consumption.

YRBSS: Multinomial logit of BMI category by sex

Male

Response Profile

Category

Frequency

Obese

838

Overweight

1346

Normal Weight

3883

Overweight

1053

Goodness of Fit

Criterion

Intercept

Intercept and Covariates

Chi-Square

63.1505***

--

AIC

16766.424

16585.989

SC

16787.036

16647.825

-2 Log L

16760.424

16567.989

Parameter

Estimate

Std Error

Intercept 1

-5.841***

0.3061

Intercept 2

-4.5836***

0.3033

Intercept 3

-1.8385***

0.2983

Age

0.2249

0.0186

Black

-0.1053

0.0681

Hispanic

0.0795

0.0559

Current Smoker

0.2298***

0.0541

Current Vaper

0.2379

0.3026

Smoke and Vape

-0.886*

0.3516

Female

Response Profile

Category

Frequency

Obese

679

Overweight

1209

Normal Weight

4245

Overweight

1162

Goodness of Fit

Criterion

Intercept

Intercept and Covariates

Chi-Square

99.9938***

--

AIC

15726.452

15596.941

SC

15747.137

15658.995

-2 Log L

15720.452

15578.941

Parameter

Estimate

Std Error

Intercept 1

-5.2196***

0.313

Intercept 2

-3.9323***

0.3102

Intercept 3

-1.1287***

0.306

Age

0.1663***

0.0192

Black

0.2912***

0.0703

Hispanic

0.2124***

0.0569

Current Smoker

0.1784***

0.0577

Current Vaper

1.1969***

0.2631

Smoke and Vape

-0.8452*

0.3287

Dependent Variable: BMI Category
***=99%, **=95%, *=90%

Table 2: YRBSS: Multinomial logit of BMI category by sex.

Results from the second specification, the quantile regression,
are listed in Table 3. Quantile regression divides the BMI distribution at the 25th, 50th and 75th percentiles and estimates
the covariates with respect to the conditional median of each
segment (Table 3). Age, race and ethnicity continue to be related
to higher BMI for both males and females at all BMI levels as
noted above. Results for electronic and conventional cigarette
use vary by gender. In the bottom 25th percentile, males see an
association between vaping and BMI, but it diminishes at higher levels. Females, however, have a positive relationship between
conventional cigarette smoking at all ranges of BMI and a
positive association with electronic cigarettes in the middle
quantile only. Interestingly, these magnitudes increase as BMI
increases suggesting a stronger relationship at higher weights.

YRBSS: Quantile regression by sex

Male

Summary Statistics

Variable

Q1

Median

Q3

Mean

Std Dev

MAD

Age

15

16

17

16.0949

1.2181

1.4826

Black

0

0

0

0.1021

0.3028

0

Hispanic

0

0

1

0.3278

0.4694

0

Vaper

0

0

1

0.2699

0.4439

0

Smoker

0

0

0

0.0256

0.1579

0

Interaction

0

0

0

0.0193

0.1375

0

lnBMI

3.0079

3.1239

3.2721

3.1515

0.2012

0.1869

Quantile Level: 0.25

Parameter

Estimate

Std Dev

--

--

--

Intercept

2.6543***

0.0459

--

--

--

--

Age

0.0217***

0.0028

--

--

--

--

Black

0.0101

0.0102

--

--

--

--

Hispanic

0.0112

0.0071

--

--

--

--

Vaper

0.0186***

0.0064

--

--

--

--

Smoker

0.0365

0.0348

--

--

--

--

Interaction

-0.0447

0.0443

--

--

--

--

Quantile Level: 0.50

Parameter

Estimate

Std Dev

--

--

--

Intercept

2.6745***

0.053

--

--

--

--

Age

0.027***

0.0032

--

--

--

--

Black

-0.0024

0.0117

--

--

--

--

Hispanic

0.0208**

0.0082

--

--

--

--

Vaper

0.0105

0.0101

--

--

--

--

Smoker

0.015

0.0381

--

--

--

--

Interaction

-0.0374

0.0517

--

--

--

--

Quantile Level: 0.75

Parameter

Estimate

Std Dev

--

--

--

Intercept

2.7921***

0.0772

--

--

--

--

Age

0.0279***

0.0047

--

--

--

--

Black

0.0053

0.0211

--

--

--

--

Hispanic

0.0387***

0.0123

--

--

--

--

Vaper

0.0138

0.014

--

--

--

--

Smoker

0.1479

0.1294

--

--

--

--

Interaction

-0.1786

0.1329

--

--

--

--

Female

Summary Statistics

Variable

Q1

Median

Q3

Mean

Std Dev

MAD

Age

15

16

17

16.0127

1.216

1.4826

Black

0

0

0

0.104

0.3053

0

Hispanic

0

0

1

0.3292

0.47

0

Vaper

0

0

0

0.223

0.4163

0

Smoker

0

0

0

0.0175

0.131

0

Interaction

0

0

0

0.0124

0.1108

0

lnBMI

3.0056

3.1035

3.2429

3.1386

0.1939

0.1691

Quantile Level: 0.25

Parameter

Estimate

Std Err

--

--

--

Intercept

2.7933***

0.0458

--

--

--

--

Age

0.0119***

0.0029

--

--

--

--

Black

0.0399***

0.0126

--

--

--

--

Hispanic

0.0363***

0.0072

--

--

--

--

Vaper

0.0125

0.0093

--

--

--

--

Smoker

0.1304**

0.0534

--

--

--

--

Interaction

-0.1064

0.0691

--

--

--

--

Quantile Level: 0.50

Parameter

Estimate

Std Err

--

--

--

Intercept

2.85***

0.0419

--

--

--

--

Age

0.0141***

0.0026

--

--

--

--

Black

0.0588***

0.0111

--

--

--

--

Hispanic

0.0503***

0.0063

--

--

--

--

Vaper

0.0252**

0.0095

--

--

--

--

Smoker

0.1846***

0.0563

--

--

--

--

Interaction

-0.1046

0.0794

--

--

--

--

Quantile Level: 0.75

Parameter

Estimate

Std Err

--

--

--

Intercept

2.903***

0.08

--

--

--

--

Age

0.0181***

0.0052

--

--

--

--

Black

0.102***

0.0181

--

--

--

--

Hispanic

0.0615***

0.011

--

--

--

--

Vaper

0.0335**

0.0161

--

--

--

--

Smoker

0.1443

0.0999

--

--

--

--

Interaction

-0.039

0.1112

--

--

--

--

Dependent Variable: lnBMI
***= 0.01, **=0.10, *=0.15

Table 3: YRBSS: Quantile regression by sex.

Discussion and Conclusion

Findings indicate that electronic and conventional cigarette use
is associated with higher BMI both male and female high school
students. The relationship is stronger among females than males
and among higher BMI individuals than low. This result could
indicate that higher BMI youth choose to smoke to curb appetite
or use it as a substitute for meals. Or show a strong preference
for all forms of consumption, both cigarettes and food. While
the underlying reason behind the association is beyond the
scope of this analysis, it exists along the BMI distribution and
appears among both genders.

While e-cigarettes have a positive BMI association, it varies by
gender along the BMI distribution. Females at the higher end
of the BMI distribution experience larger associations between
electronic cigarettes and BMI, while males at the lower end tend
find a stronger relationship. The positive association could result
from the 100-140 calories in every ounce of vaping liquid and
added sweeteners and flavors, though it is not clear how many of
these calories are ingested after the liquid is vaporized. It could
also be related to post-cessation weight gain by respondents
who use e-cigarettes as a substitute product when trying to quit
smoking. There is anecdotal evidence that e-cigarettes make
users feel more sluggish after continued use, but no scientific
evidence exists to support this claim.

While causality is outside the scope of this analysis, these results
raise many questions regarding the BMI and other health effects
of adolescent smoking. The popularity of electronic cigarettes
has increased rapidly, while that of conventional cigarettes has
declined [30,32]. Adolescence is a time marked by exploration
and experimentation [33]. Sensation seeking, or the need for
new experiences combined with the willingness to take risks
to achieve them, has been associated with adolescent substance
use and may increase cigarette/e-cigarette experimentation [34].

While limited in several ways, this analysis utilizes a nationally
representative sample of individuals from all states, races,
ethnic groups and income classes. It employs statistical tools
to account for weighted estimation, covariate dispersion and
cross-sectional panel integration. By providing insight into the
prevalence, patterns and correlates of adolescent conventional and e-cigarette use, this study underscores the need for further
research assessing whether e-cigarettes may curb weight gain or
enable current cigarette smokers to curb use. Motives and risk
factors currently remain unresolved. More research is needed
to understand safety and health effects of e-cigarette use in
adolescents.

Funding

The author reports no monetary interests in the publication of
this manuscript. No external funding was used in the research
contained herein.

Ethical Approval Disclosure

This manuscript does not contain any studies with human
participants or animals performed by the author.

Conflicts of Interest Disclosure

The author certifies that he/she has NO affiliations with or
involvement in any organization or entity with any financial
interest (such as honoraria; educational grants; participation in
speakers’ bureaus; membership, employment, consultancies,
stock ownership, or other equity interest; and expert testimony
or patent-licensing arrangements), or non-financial interest (such
as personal or professional relationships, affiliations, knowledge
or beliefs) in the subject matter or materials discussed in this
manuscript.

References

McMillen RC, Gottlieb MA, Shaefer RM, et al. Trends in electronic cigarette use among US adults: Use is increasing in both smokers and nonsmokers. Nicotine Tob Res. 2014;17:1195-202.

Centers for Disease Control and Prevention (CDC). Notes from the field: Electronic cigarette use among middle and high school students-United States, 2011-2012. MMWR. Morbidity and Mortality Weekly Report. 2013;62:729-30.

Vardavas C, Filippidis F, Agaku I. Determinants and prevalence of e-cigarette use throughout the European Union: A secondary analysis of 26 566 youth and adults from 27 Countries. Tob Control. 2015;24:442?8.

Polanska K, Wojtysiak P, Bak-Romaniszyn L, et al. Susceptibility to cigarette smoking among secondary and high school students from a socially disadvantaged rural area in Poland. Tob Induc Dis. 2016;14:14-28.

Goniewicz ML, Zielinska-Danch W. Electronic cigarette use among teenagers and young adults in Poland. Pediatrics. 2012;130:e879.